THE DIFFERENCE BETWEEN DATA ANALYSIS AND ATTRIBUTION

THE DIFFERENCE BETWEEN DATA ANALYSIS AND ATTRIBUTION

What is the difference between data analysis and data attribution?

The marketing science of collecting and understanding your data became more and more robust over the last years. Companies with multiple digital and offline marketing channels have been struggling to define their marketing strategies due to the large amounts of marketing data they get from these sources. This can be overwhelming if you don’t have a clear idea of where to concentrate your marketing efforts.

Data is useless unless you turn it into a source of concrete solutions by understanding the meaning of all of your numbers and how they can effectively drive your actions. An adequate marketing measurement can help you understand the real needs of your customers and how they engage with your brand. Both are key in making future plans and coming up with a solid marketing strategy.

A strategic marketing measurement doesn’t return the best form your data by only measuring how the audience interacts with your site. To deeply receive a full picture of your data, you need to blend analytics with a solid attribution insight. Understanding the actual needs of your customers through an adequate marketing measurement is key. Let’s be honest, any planning for the future begins by understanding how your customers engage with your brand.

Why is data analysis not enough?

Data analytics is a powerful strategic marketing measurement. It refers to any data feedback about customer behavior before and after any touch point. A company cannot build a successful digital strategy without using analytics to collect insights. Analytics are key in understanding a company’s digital marketing performance and measuring the client’s interaction with the brand’s online properties.

Analytics can collect data feedback that concerns your client behavior. If you need to discover what keywords return the highest earnings, what sections of your site have the highest engagement, or what pages are losing visitors, then analytics can help you obtain useful insights.

However, something is missing. Although this data can significantly help companies make strategic decisions around their digital marketing strategy, it may not be enough.

By using data attribution, a company can determine CLV. The CLV defines the maximum revenue potential of a particular customer or customer type, allowing companies to focus their efforts on individuals and groups with a high CLV.

Data-driven attribution uses sophisticated algorithms that lead you towards realistic and complete insights that will help you drive your marketing efforts.

When comparing each interaction and conversion path you need to be aware of all effective touchpoints over a customers’ journey. This is necessary because they are all relevant components used to leverage the ultimate results.

An algorithmic attribution model allows you to effectively measure the most relevant KPIs for highly integrated multi-channel campaignsincluding profit, ROI, CLV, and sales. It is important to have a clear understanding of the attribution data in order to improve how the companies are making their investments for running ads or creating contents and optimize their marketing strategy.